
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     Tuple=None)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   None
							if token is not None:
														__UpperCamelCase      :  List[str]       =   {"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}

							__UpperCamelCase      :  Optional[int]       =   F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
							__UpperCamelCase      :  Optional[int]       =   requests.get(_lowerCamelCase		,    headers=_lowerCamelCase).json()
							__UpperCamelCase      :  Optional[Any]       =   {}

							try:
														job_links.update({job["name"]: job["html_url"] for job in result["jobs"]})
														__UpperCamelCase      :  List[str]       =   math.ceil((result["total_count"] - 100) / 100)

														for i in range(_lowerCamelCase):
																					__UpperCamelCase      :  Dict       =   requests.get(url + F'&page={i + 2}'		,    headers=_lowerCamelCase).json()
																					job_links.update({job["name"]: job["html_url"] for job in result["jobs"]})

														return job_links
							except Exception:
														print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}')

							return {}




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Optional[Any]=None)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Dict       =   None
							if token is not None:
														__UpperCamelCase      :  Any       =   {"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}

							__UpperCamelCase      :  Dict       =   F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
							__UpperCamelCase      :  List[str]       =   requests.get(_lowerCamelCase		,    headers=_lowerCamelCase).json()
							__UpperCamelCase      :  Union[str, Any]       =   {}

							try:
														artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]})
														__UpperCamelCase      :  Union[str, Any]       =   math.ceil((result["total_count"] - 100) / 100)

														for i in range(_lowerCamelCase):
																					__UpperCamelCase      :  Any       =   requests.get(url + F'&page={i + 2}'		,    headers=_lowerCamelCase).json()
																					artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]})

														return artifacts
							except Exception:
														print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}')

							return {}




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Dict		,    _lowerCamelCase					:     Tuple)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[int]       =   None
							if token is not None:
														__UpperCamelCase      :  Tuple       =   {"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}

							__UpperCamelCase      :  Optional[Any]       =   requests.get(_lowerCamelCase		,    headers=_lowerCamelCase		,    allow_redirects=_lowerCamelCase)
							__UpperCamelCase      :  str       =   result.headers["Location"]
							__UpperCamelCase      :  Optional[Any]       =   requests.get(_lowerCamelCase		,    allow_redirects=_lowerCamelCase)
							__UpperCamelCase      :  Any       =   os.path.join(_lowerCamelCase		,    F'{artifact_name}.zip')
							with open(_lowerCamelCase		,    "wb") as fp:
														fp.write(response.content)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Dict		,    _lowerCamelCase					:     Any=None)   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   []
							__UpperCamelCase      :  List[Any]       =   []
							__UpperCamelCase      :  Union[str, Any]       =   None

							with zipfile.ZipFile(_lowerCamelCase) as z:
														for filename in z.namelist():
																					if not os.path.isdir(_lowerCamelCase):
																												# read the file
																												if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																																			with z.open(_lowerCamelCase) as f:
																																										for line in f:
																																																	__UpperCamelCase      :  Union[str, Any]       =   line.decode("UTF-8").strip()
																																																	if filename == "failures_line.txt":
																																																								try:
																																																															# `error_line` is the place where `error` occurs
																																																															__UpperCamelCase      :  Dict       =   line[: line.index(": ")]
																																																															__UpperCamelCase      :  List[Any]       =   line[line.index(": ") + len(": ") :]
																																																															errors.append([error_line, error])
																																																								except Exception:
																																																															# skip un-related lines
																																																															pass
																																																	elif filename == "summary_short.txt" and line.startswith("FAILED "):
																																																								# `test` is the test method that failed
																																																								__UpperCamelCase      :  Tuple       =   line[len("FAILED ") :]
																																																								failed_tests.append(_lowerCamelCase)
																																																	elif filename == "job_name.txt":
																																																								__UpperCamelCase      :  Dict       =   line

							if len(_lowerCamelCase) != len(_lowerCamelCase):
														raise ValueError(
														    F'`errors` and `failed_tests` should have the same number of elements. Got {len(_lowerCamelCase)} for `errors` '
														    F'and {len(_lowerCamelCase)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
														    " problem.")

							__UpperCamelCase      :  Optional[Any]       =   None
							if job_name and job_links:
														__UpperCamelCase      :  Union[str, Any]       =   job_links.get(_lowerCamelCase		,    _lowerCamelCase)

							# A list with elements of the form (line of error, error, failed test)
							__UpperCamelCase      :  int       =   [x + [y] + [job_link] for x, y in zip(_lowerCamelCase		,    _lowerCamelCase)]

							return result




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any]=None)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   []

							__UpperCamelCase      :  Dict       =   [os.path.join(_lowerCamelCase		,    _lowerCamelCase) for p in os.listdir(_lowerCamelCase) if p.endswith(".zip")]
							for p in paths:
														errors.extend(get_errors_from_single_artifact(_lowerCamelCase		,    job_links=_lowerCamelCase))

							return errors




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Any=None)   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   Counter()
							counter.update([x[1] for x in logs])
							__UpperCamelCase      :  Tuple       =   counter.most_common()
							__UpperCamelCase      :  List[Any]       =   {}
							for error, count in counts:
														if error_filter is None or error not in error_filter:
																					__UpperCamelCase      :  Tuple       =   {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}

							__UpperCamelCase      :  Optional[int]       =   dict(sorted(r.items()		,    key=lambda _lowerCamelCase: item[1]["count"]		,    reverse=_lowerCamelCase))
							return r




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Any)   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[Any]       =   test.split("::")[0]
							if test.startswith("tests/models/"):
														__UpperCamelCase      :  Union[str, Any]       =   test.split("/")[2]
							else:
														__UpperCamelCase      :  int       =   None

							return test




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     int=None)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   [(x[0], x[1], get_model(x[2])) for x in logs]
							__UpperCamelCase      :  List[Any]       =   [x for x in logs if x[2] is not None]
							__UpperCamelCase      :  Optional[Any]       =   {x[2] for x in logs}

							__UpperCamelCase      :  str       =   {}
							for test in tests:
														__UpperCamelCase      :  Union[str, Any]       =   Counter()
														# count by errors in `test`
														counter.update([x[1] for x in logs if x[2] == test])
														__UpperCamelCase      :  int       =   counter.most_common()
														__UpperCamelCase      :  Union[str, Any]       =   {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
														__UpperCamelCase      :  Union[str, Any]       =   sum(error_counts.values())
														if n_errors > 0:
																					__UpperCamelCase      :  str       =   {"count": n_errors, "errors": error_counts}

							__UpperCamelCase      :  int       =   dict(sorted(r.items()		,    key=lambda _lowerCamelCase: item[1]["count"]		,    reverse=_lowerCamelCase))
							return r




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int])   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   "| no. | error | status |"
							__UpperCamelCase      :  Any       =   "|-:|:-|:-|"
							__UpperCamelCase      :  Tuple       =   [header, sep]
							for error in reduced_by_error:
														__UpperCamelCase      :  Optional[int]       =   reduced_by_error[error]["count"]
														__UpperCamelCase      :  Optional[Any]       =   F'| {count} | {error[:100]} |  |'
														lines.append(_lowerCamelCase)

							return "\n".join(_lowerCamelCase)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any])   ->       List[str]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   "| model | no. of errors | major error | count |"
							__UpperCamelCase      :  int       =   "|-:|-:|-:|-:|"
							__UpperCamelCase      :  Optional[int]       =   [header, sep]
							for model in reduced_by_model:
														__UpperCamelCase      :  List[str]       =   reduced_by_model[model]["count"]
														__UpperCamelCase  ,		__UpperCamelCase      :  Union[str, Any]       =   list(reduced_by_model[model]["errors"].items())[0]
														__UpperCamelCase      :  Union[str, Any]       =   F'| {model} | {count} | {error[:60]} | {_count} |'
														lines.append(_lowerCamelCase)

							return "\n".join(_lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Any						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
		parser.add_argument(
		    '--output_dir',
		    type=str,
		    required=True,
		    help='Where to store the downloaded artifacts and other result files.',
		)
		parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
		lowercase    :   List[Any]						=			parser.parse_args()

		os.makedirs(args.output_dir, exist_ok=True)

		lowercase    :   Tuple						=			get_job_links(args.workflow_run_id, token=args.token)
		lowercase    :   List[Any]						=			{}
		# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
		# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
		if _job_links:
				for k, v in _job_links.items():
						# This is how GitHub actions combine job names.
						if " / " in k:
								lowercase    :   Tuple						=			k.find(' / ')
								lowercase    :   Union[str, Any]						=			k[index + len(' / ') :]
						lowercase    :   Union[str, Any]						=			v
		with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
				json.dump(job_links, fp, ensure_ascii=False, indent=4)

		lowercase    :   List[str]						=			get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
				json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		for idx, (name, url) in enumerate(artifacts.items()):
				download_artifact(name, url, args.output_dir, args.token)
				# Be gentle to GitHub
				time.sleep(1)

		lowercase    :   List[str]						=			get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		lowercase    :   Union[str, Any]						=			Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		lowercase    :   str						=			counter.most_common(30)
		for item in most_common:
				print(item)

		with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
				json.dump(errors, fp, ensure_ascii=False, indent=4)

		lowercase    :   Dict						=			reduce_by_error(errors)
		lowercase    :   Any						=			reduce_by_model(errors)

		lowercase    :   str						=			make_github_table(reduced_by_error)
		lowercase    :   List[str]						=			make_github_table_per_model(reduced_by_model)

		with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)
		with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)